The present embodiments relate to computer assisted clinical decision support. In particular, computer assisted medical decision support incorporates a medical ontology.
Medical ontologies provide information associated with one or more diseases and numerous medically relevant concepts (e.g., laboratory and diagnostic procedures; physiologic, biologic, genetic, molecular functions; organs and body parts; diseases, symptoms, and medical findings; etc). Different relationships between concepts are reflected by the medical ontology. For example, different names for a same disease are provided in an “IS A” type relationship. Related morphologies (e.g., inflammation) and body location are other types of relationships in the medical ontology. Medical ontologies may also contain various terms associated to a medical concept representing the same (or similar) meaning for the concept.
Medical ontologies provide information for computer assisted medical decision support. Computer assisted medical decision support systems may be deterministic. For example, a rule-based system alerts clinicians to drug-drug interaction. The rules are determined manually from the medical ontology.
Rule-based systems may support only a fraction of medical decisions. Rule-based systems typically require structured input (e.g., billing, demographic, lab, pharmacy or other rigidly formatted or input information). However, medical information used in medical decisions may be in an unstructured format (e.g., text, physician notes, or images). Rule-based systems may have incomplete information.
Medical decision-making is frequently probabilistic, so a deterministic, rule-based system may not adequately support such decisions. Simplistic combinations of multiple “IS A” type relationships input to the system indicating a greater chance of having the disease have been used. For example, a greater number of terms with an “IS A” relationship indicates a greater chance of having a disease. However, this simple approach may not accurately reflect probabilities.
More complex probabilistic inference systems have been used for medical decision support. Such systems are often hard to build, requiring finely tuned domain knowledge coded by hand. These systems are built on a network of concepts elicited, painstakingly, from physicians. Further, these systems require precise probabilities to be set, but such probabilities are hard to find. Physicians implicitly perform probabilistic inference very well in day-to-day work, but find it very hard to set precise numerical probabilities when asked. Once created, these systems are hard to maintain. As medical knowledge changes, the systems are changed. Making additions or deletions to such systems is difficult due to the need to identify the differences and again assign probabilities. These systems also work off structured patient data.